Enhancing cybersecurity in IoT systems: a hybrid deep learning approach for real-time attack detection
Abstract Cybersecurity risks have increased due to the growing ubiquity of Internet of Things (IoT) technology, making attack and anomaly detection a major concern. IoT systems face growing threats from attacks such as Distributed Denial of Service (DDoS), Denial of Service (DoS), Probing, R2L (Remo...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Internet of Things |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43926-025-00156-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849401954231386112 |
|---|---|
| author | Mohammad Zahid Taran Singh Bharati |
| author_facet | Mohammad Zahid Taran Singh Bharati |
| author_sort | Mohammad Zahid |
| collection | DOAJ |
| description | Abstract Cybersecurity risks have increased due to the growing ubiquity of Internet of Things (IoT) technology, making attack and anomaly detection a major concern. IoT systems face growing threats from attacks such as Distributed Denial of Service (DDoS), Denial of Service (DoS), Probing, R2L (Remote to Local), U2R (User to Root), Malware, Scanning, Port Scan, and Bot, all of which can seriously jeopardize system integrity, as they become more and more integrated into multiple domains. This paper proposes a hybrid deep learning model (CNN-BiLSTM) using CNN combined with Bidirectional Long Short-Term Memory (Bi-LSTM) for the better detection of such attacks in real-time. The proposed hybrid deep learning model (CNN-BiLSTM) was extensively evaluated on three benchmark datasets, namely KDDCup99, NSL-KDD, and CIC_IDS_2017. The proposed model exhibited outstanding performance on these datasets and yielded an accuracy of 99.9% on KDDCup99, 99.8% on NSL-KDD, and 98.0% on CIC_IDS_2017. Precision, recall, and F1 scores are similar in all attack categories, especially on complex threats such as DoS, DDoS, and malware. A comparison study with the state-of-the-art technique reflects our proposed model's superiority in terms of precision and recall. It offers a good lead toward real-world application and could be piloted in IoT environments by integrating into real-time security platforms to mitigate progressive cyber threats. The contribution of this work is a robust and scalable solution in the fast-growing IoT security area to both present and future challenges in securing critical infrastructures. |
| format | Article |
| id | doaj-art-bb7b0022fb444bbfa0216011039fa3cb |
| institution | Kabale University |
| issn | 2730-7239 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Internet of Things |
| spelling | doaj-art-bb7b0022fb444bbfa0216011039fa3cb2025-08-20T03:37:40ZengSpringerDiscover Internet of Things2730-72392025-07-015113110.1007/s43926-025-00156-yEnhancing cybersecurity in IoT systems: a hybrid deep learning approach for real-time attack detectionMohammad Zahid0Taran Singh Bharati1Department of Computer Science, Jamia Millia IslamiaDepartment of Computer Science, Jamia Millia IslamiaAbstract Cybersecurity risks have increased due to the growing ubiquity of Internet of Things (IoT) technology, making attack and anomaly detection a major concern. IoT systems face growing threats from attacks such as Distributed Denial of Service (DDoS), Denial of Service (DoS), Probing, R2L (Remote to Local), U2R (User to Root), Malware, Scanning, Port Scan, and Bot, all of which can seriously jeopardize system integrity, as they become more and more integrated into multiple domains. This paper proposes a hybrid deep learning model (CNN-BiLSTM) using CNN combined with Bidirectional Long Short-Term Memory (Bi-LSTM) for the better detection of such attacks in real-time. The proposed hybrid deep learning model (CNN-BiLSTM) was extensively evaluated on three benchmark datasets, namely KDDCup99, NSL-KDD, and CIC_IDS_2017. The proposed model exhibited outstanding performance on these datasets and yielded an accuracy of 99.9% on KDDCup99, 99.8% on NSL-KDD, and 98.0% on CIC_IDS_2017. Precision, recall, and F1 scores are similar in all attack categories, especially on complex threats such as DoS, DDoS, and malware. A comparison study with the state-of-the-art technique reflects our proposed model's superiority in terms of precision and recall. It offers a good lead toward real-world application and could be piloted in IoT environments by integrating into real-time security platforms to mitigate progressive cyber threats. The contribution of this work is a robust and scalable solution in the fast-growing IoT security area to both present and future challenges in securing critical infrastructures.https://doi.org/10.1007/s43926-025-00156-yIoT securityDeep learningIDSCNNBi-LSTMIoT attacks |
| spellingShingle | Mohammad Zahid Taran Singh Bharati Enhancing cybersecurity in IoT systems: a hybrid deep learning approach for real-time attack detection Discover Internet of Things IoT security Deep learning IDS CNN Bi-LSTM IoT attacks |
| title | Enhancing cybersecurity in IoT systems: a hybrid deep learning approach for real-time attack detection |
| title_full | Enhancing cybersecurity in IoT systems: a hybrid deep learning approach for real-time attack detection |
| title_fullStr | Enhancing cybersecurity in IoT systems: a hybrid deep learning approach for real-time attack detection |
| title_full_unstemmed | Enhancing cybersecurity in IoT systems: a hybrid deep learning approach for real-time attack detection |
| title_short | Enhancing cybersecurity in IoT systems: a hybrid deep learning approach for real-time attack detection |
| title_sort | enhancing cybersecurity in iot systems a hybrid deep learning approach for real time attack detection |
| topic | IoT security Deep learning IDS CNN Bi-LSTM IoT attacks |
| url | https://doi.org/10.1007/s43926-025-00156-y |
| work_keys_str_mv | AT mohammadzahid enhancingcybersecurityiniotsystemsahybriddeeplearningapproachforrealtimeattackdetection AT taransinghbharati enhancingcybersecurityiniotsystemsahybriddeeplearningapproachforrealtimeattackdetection |